National Data Platform Pilot: Services for Equitable Open Access to Data.

Topology-Aware Data Compression for Scientific Analysis and Visualization.

PIs: Bei Wang Phillips (U of Utah); Hanqi Guo (Ohio State), Xin Liang (UKY).

Abstract: Today’s large-scale simulations are producing vast amounts of data that are revolutionizing scientific thinking and practices. As the disparity between data generation rates and available I/O bandwidths continues to grow, data storage and movement are becoming significant bottlenecks for extreme-scale scientific simulations in terms of in situ and post hoc analysis and visualization. Such a disparity necessitates data compression, where data produced by simulations are compressed in situ and decompressed in situ and post hoc for analysis and exploration. Meanwhile, topological data analysis plays an important role in extracting insights from scientific data regarding feature definition, extraction, and evaluation. However, most of today’s lossy compressors are topology- agnostic, i.e., they do not guarantee the preservation of topological features essential to scientific discoveries. This project aims to research and develop advanced lossy compression techniques and softwares that preserve topological features in data for in situ and post hoc analysis and visualization at extreme scales. The data of interest are scalar fields and vector fields that arise from scientific simulations, with driving applications in cosmology, climate, and fusion simulations. This project has three research thrusts that focus on deriving topological constraints from scalar fields (I) and vector fields (II), and integrating these constraints to develop topology-aware error-controlled and neural compressors (III).

Multiparameter Topological Data Analysis

PIs: Bei Wang Phillips (U of Utah); Facundo Memoli (Ohio State), Tamal Dey (Purdue)

Abstract: Although TDA involving a single parameter has been well researched and developed, the same is not true for the multiparameter case. At its current nascent stage, multiparameter TDA is yet to develop tools to practically handle complex, diverse, and high-dimensional data. To meet this challenge, this project will make both mathematical and algorithmic advances for multiparameter TDA. To scope effectively, focus will be mainly on three research thrusts to: (I) explore multiparameter persistence for generalized features and develop algorithms to compute them; (II) exploit the connections of zigzag persistence to multiparameter settings to support dynamic data analysis, and (III) generalize topological descriptors such as merge trees, Reeb spaces, and mapper. The overarching goal of lacing all three thrust areas remains that of developing actionable and practicable tools in applications including Cytometry, Materials Science, Climate Simulations and Ecology.

Title: CranioRate: An imaging-based, deep-phenotyping analysis toolset, repository, and online clinician interface for craniosynostosis

Agency: NIH

Dates: 2023-2028

PIs: Ross Whitaker, Utah; Jesse Goldstein, UPitt

Co investigators: Shireen Elhabian, Utah; Kamlesh Patel, WashU, Michael Golinko, Vanderbilt; Jordan Swanson, UPenn; Carl Kesselman, USC

Abstract: The purpose of this research grant application is to build on the advanced machine learning (ML) tool developed as part of a pilot study (R21EB026061) that objectively quantifies cranial dysmorphology, or deep phenotypes, in patients with metopic craniosynostosis (MC). Abnormal cranial suture fusion (craniosynostosis) occurs in one of every 2500 infants born in the US, resulting in disrupted regional skull growth and an increased risk of elevated intracranial pressure, neurocognitive impairment and visual disturbances including blindness. Impaired skull growth along the fused suture and subsequent growth compensation in other areas of the skull lead to predictable head shape patterns in patients with craniosynostosis; surgery is recommended early in childhood to restore normal head shape and prevent neurocognitive sequelae.

A hybrid computational-experimental framework for targeted embolization in vascular disease

NIH Trailblazer Award

Dates: 2023-2026

PIs: Amir Arzani, Utah; Jingjie He, NCSU (MPI proposal)

Abstract: Minimally invasive transcatheter embolization is a common nonsurgical procedure in interventional radiology used for the deliberate occlusion of blood vessels for the treatment of diseased or injured vasculature. One of the most commonly used embolic agents for clinical practice is microsphere. No systemic platform has been developed to investigate the correlation between microsphere properties and embolic outcomes. More importantly, clinicians have no technology for estimating the trajectory of emboli, and as such significant uncertainty exists in embolization treatment. In this proposal, we will develop, for the first time, a two-way interactive biomaterial-computational platform that will 1) offer rational design of multifunctional microspheres, 2) accurately guide the transcatheter location for microsphere deployment, and 3) predict microsphere in vivo trajectory and their aggregation in the vasculature to maximize embolic success for personalized therapies. We envision that this innovative technology can be applied to liquid embolic agents, and also be widely disseminated to the treatment of diverse vascular conditions, such as prostate hyperplasia, liver tumors, and fibroids, for translation to patient-specific therapy.

Understanding complex wind-driven wildfire propagation patterns with a dynamical systems approach

NSF EAGER grant

Dates: 2023-2024

PI: Amir Arzani, Utah

Co-PIs: Rob Stoll, Utah (ME); Fatemeh Afghah, Clemson; Ali Tohidi, SJSU.

Abstract: Intense and long wildfire seasons have unfortunately become a normal routine in certain parts of the US. Dangerous wildfires are often driven by intense winds. The chaotic nature of wind patterns makes prediction and fundamental understanding of wildfire growth a challenging task. In this study, dynamical systems theory will be employed to define coherent structures customized to the transport problems used to model wind-driven wildfire growth. A set of benchmark problems motivated by the field of dynamical systems and chaotic advection together with more complex realistic wind patterns will be leveraged to study the role of coherent structures in wildfire growth. Specifically, the hypothesis that generalized Lagrangian coherent structures could be defined to provide a template for wildfire growth under certain scenarios will be explored. This study will provide a new theory that not only simplifies our understanding of wildfire growth under complex wind patterns but also guides wildfire management and mitigation.